Book Image

Machine Learning with Swift

By : Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev
Book Image

Machine Learning with Swift

By: Jojo Moolayil, Alexander Sosnovshchenko, Oleksandr Baiev

Overview of this book

Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Table of Contents (18 chapters)
Title Page
Packt Upsell

People motion recognition using inertial sensors

Wouldn't it be awesome at the end of every day to see the statistics of it: how much time have you spent doing things you like, and how much time you've wasted? With this kind of report, you could make your time management decisions based on real data, not just a gut feeling. Wait, but there are a lot of time trackers out there on the App Store, right? Sure, but there is one problem with most of them: you have to fill them in manually, because they can't detect what are you doing at every moment. You can't teach them to recognize types of your activities. Fortunately, we can fix this using machine learning; specifically, time series classification.


Time series is a special kind of dataset in which samples are arranged according to the time. Usually, time series are generated when samples are taken repeatedly after equal time intervals (sampling interval). In other words, the time series is a sequence of values measured at successive moments...